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Planning a path for a mobile robot typically requires building a map (e.g.,an occupancy grid) of the environment as the robot moves around. Whilenavigating in an unknown environment, the map built by the robot online mayhave many as-yet-unknown regions. A conservative planner may avoid such regionstaking a longer time to navigate to the goal. Instead, if a robot is able tocorrectly predict the occupancy in the occluded regions, the robot may navigateefficiently. We present a self-supervised occupancy prediction technique,ProxMaP, to predict the occupancy within the proximity of the robot to enablefaster navigation. We show that ProxMaP generalizes well across realistic andreal domains, and improves the robot navigation efficiency in simulation by12.40% against a traditional navigation method. We share our findings and codeat https://raaslab.org/projects/ProxMaP.

ProxMaP: Proximal Occupancy Map Prediction for Efficient Indoor Robot Navigation